Detailed overview:
Chloroplast Transit Peptide Prediction
Title: Chloroplast Transit Peptide Prediction
Title abbreviated: ChloroP
Creator: Emanuelsson, Olof; Nielsen, Henrik; vonHeijne, Gunnar
Publisher: Technical University of Denmark /Center for Biological Sequence Analysis / BioCenterum-DTU
Abstract: We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the so far only publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within +- 2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 A. thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. [Information of the supplier]
Sequence Motif Prediction
Subject: Cell biology (571.6);
DNA (Desoxyribonucleic acid) (572.86)
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Audience: Experts
Language: English
Format: website
Resource type: Discipline based websites
Access: free
Metadata update date: 2011-08-17
Metadata provider: UBFfm
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